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Design-hour volume (DHV) and directional DHV (DDHV) are important traffic forecast parameters for both planning and operational studies. They are used for roads and intersection design and operational analysis. Estimating these two parameters requires a record of hourly volumes for every hour in a year. Therefore, permanent traffic counters are usually used to keep a record of those hourly volumes. The use of permanent counters faces several challenges because of adjacent construction activities and hardware or communication failure. These challenges result in the missing part of the collected data. Moreover, estimating DHV and DDHV based on short-term traffic counts is often needed. In this research, an artificial intelligence approach is used to estimate DHV and DDHV for roadways with different functional classifications. An artificial neural network model, which utilises historical records of annual average daily traffic along with other road characteristics, such as number of lanes and functional classification, is developed. Results show that the model was able to achieve a highly accurate and reliable DHV and DDHV estimates.

Macroscopic models that can be used to estimate the delay at signalized intersections caused by downstream traffic disturbances, such as queues in closely spaced settings, are presented. The models use basic traffic flow properties and control parameters at neighboring intersections. The models are applied to a hypothetical two-signal system. The results show that the delay caused by downstream disturbances can be significant and therefore must be included in level-of-service and capacity analyses. The results also show that control parameters, such as offsets and green splits at upstream and downstream approaches, play key roles in reducing delay. Validation by the use of microscopic traffic simulation shows close agreement between the delay estimates from the proposed models and that from the microscopic simulation model.

Shoulder rumble strips and enhanced pavement markings were two key areas identified by AASHTO to reduce runoff-road collisions. The Michigan Department of Transportation is currently testing the effectiveness of placing shoulder rumble strips close to the edge of travel lanes and painting a line on the rumble strip to improve the retroreflective properties of the line. Two issues arise from placing the rumble strips closer to the edge line and moving the paint line onto the rumble strip: the potential increased noise when vehicles crossed over the rumble strips and damage to the pavement if vehicles moved closer to the construction joint between the travel lane and the shoulder. This study found that simply moving the paint line onto the rumble strips resulted in vehicles moving slightly closer to the edge of the travel lane. However, maintaining the current edge line and adding an additional paint line on the rumble strip resulted in vehicles moving away from the edge of the pavement and thus reduced noise and potential damage to the pavement.

The formulation and solution of a dynamic signal control and queue management algorithm for two-way oversaturated arterials are presented. The algorithm is structured to find optimal control and queue management in at least one direction: the primary direction. The other direction is optimized only subject to the fulfillment of the constraints on the primary direction. The main idea of the procedure is to manage queue formation and dissipation through proper design of signal control parameters such that queues are always contained within respective links and that spillbacks are prevented. The two-way control procedure presented accounts for all possible traffic flow regimes that may form as a result of queue management schemes. Genetic algorithms were used to solve the problem. The results show that although the algorithm provides optimal control and queue management in the primary direction, it was also able to manage queues in the secondary direction so that the occurrence of queue spillbacks is prevented. Progression was attainable only in the primary direction. However, the algorithm was able to effectively deal with evolving traffic queues in both directions. In all but one link, traffic was successfully contained in the respective links, thus preventing spillback.

Models for estimation of the capacities of oversaturated arterials were developed. The input variables in these models are capacities of individual intersections, offsets, and vehicle queue lengths. Models for quantification of capacity loss due to blockage caused by downstream queues are also presented. The proposed models show that when arterial capacity is determined in oversaturated conditions, it is not sufficient to consider only the capacities of critical intersections; instead, the capacities of critical subsystems must be considered. A critical subsystem is any two intersections plus the link that joins them where traffic processing capability is the lowest. This traffic processing capability, or critical subsystem capacity, determines the arterial capacity. It is a function of the capacities of the respective intersections, the offset between them, and the queue length on the link joining them. It is shown that a critical subsystem is not unique in that it may change location over the course of the study period. To minimize capacity loss, it is shown that offsets must be an explicit function of queue lengths. The practical use of the models was demonstrated for an oversaturated two-intersection system. The results show that improper setting of offsets can lead to significant capacity loss. In extreme cases all capacity in a given cycle may be lost if the offsets are not set properly.

Emerging artificial intelligence techniques such as genetic algorithms (GAs) allow a more realistic representation and solution of difficult and combinatorial problems such as the dynamic traffic queue management problem. Computational experience in solving such complex large-scale problems by use of micro-GAs is described. In addition to providing evidence of the ability of micro-GAs to successfully identify optimal traffic management schemes, some micro-GA-associated computational issues that warrant attention are highlighted. Choosing a proper population size is a critical decision, and internal variability must be accounted for to assess the goodness of the optimization results properly. A simple rule for deciding the best population size for micro-GAs is proposed. Micro-GAs may converge to low-quality solutions, particularly with very small population sizes; convergence of micro-GAs by itself is not a sufficient indication of good performance. The size of the search space for some real-world systems can pose some difficulties to micro-GAs. Choosing between micro-GAs and regular GAs is a problem-dependent decision.

The formulation and solution of a new algorithm for queue management and coordination of traffic signals along oversaturated arterials are presented. Existing traffic-control and signal-coordination algorithms deal only with undersaturated steady-state traffic flow conditions. No practical algorithms are readily available for oversaturated flow conditions. The main idea of the procedure is to manage queue formation and dissipation on system links so that traffic flow is maximized by efficiently using all green time, preventing formation of de facto red, accounting for the non-steady-state conditions, and providing time-dependent control measures. The problem is formulated as a throughput maximization problem subject to state and control variables. The solution is then obtained using genetic algorithms. The results show that the control procedure can produce dynamic and responsive control so that traffic progression is attained and all undesirable conditions such as queue spill-back and de facto red are avoided.

Models to estimate right-turn-on-red (RTOR) volumes at intersections with exclusive right-turn (RT) lanes are developed, and the effects of RTOR volumes on computed delay are assessed. The important variables in these models are the RT volume, followed by green-time-to-cycle (G/C) ratio, volume of conflicting traffic, and whether there is a protected phase for opposing left-turning vehicles. The estimated RTOR increased as the RTs increased. However, it decreased as G/C and the volume of conflicting traffic increased. Results show that not accounting for RTOR volumes can lead to a significant difference in delay estimates for RT lanes and, to a lesser extent, on the corresponding approaches. For RT lanes, in one-half of the cases the difference was greater than 5 percent, in more than one-quarter of the cases the difference was greater than 10 percent, and in at least one of eight cases the difference was greater than 20 percent. Differences for individual cases ranged between 0 and 130 percent, with an average of 12 percent. For approaches, the average delay difference was 4 percent, and for individual cases the difference ranged between −2 and 78 percent. As recommended by the Highway Capacity Manual, actual field counts of RTOR volume should be used whenever available. However, in the absence of such counts, the models developed here can be used and hence should be considered in capacity analysis procedures.